ENGLISH ABSTRACT: Construction projects are risky by nature, with many variables a ecting their outcome.
A contingency cost and duration are allocated to the budget and schedule
of a project to provide for the possible impact of risks.
To enable the management of project-related risk on a portfolio level, contingency
estimation must be performed consistently and objectively. The current
project contingency estimation method used in the capital program management
department of Eskom Distribution Western Cape Operating Unit is not standardised,
and is based solely on expert opinion. The aim of the study was to
develop a contingency estimation tool to decrease the in
uence of subjectivity on
contingency estimation methods throughout the project lifecycle so as to enable
consistent project risk re
ection on a portfolio level.
From a review of contingency estimation approaches in literature, a hybrid
method combining neural network analysis of systemic risks and expected value
analysis of project-speci c risks was chosen.
Interviews were conducted with project managers (regarding network asset
construction projects completed in the last two nancial years) to distinguish
systemic and project-speci c risk impact on cost and duration growth. Outputs
from 22 interviews provided three data patterns for each of 89 projects. After interview
data processing, 138 training patterns pertaining to 85 projects remained
for neural network training, validation and testing.
Six possible neural network inputs (systemic risk drivers) were selected as
project de nition level, cost, duration, business category, voltage category and
job category. A multilayer feedforward neural network was trained using a supervised training approach combining a multi-objective simulated annealing algorithm
with the standard backpropagation algorithm.
Neural network results were evaluated for di erent scenarios considering possible
combinations of model input variables and number of hidden nodes. The
best scenario (exclusion of business category input with nine hidden nodes) was
chosen based on training and validation errors. Validation error levels are comparable
to those of similar studies in the project management eld. The chosen
scenario was shown to outperform multiple linear regression, but calculated R2
values were lower than anticipated. It is expected that neural network performance
will further improve as additional training patterns become available.
The trained neural network was combined with an expected value analysis
tool (risk register format) to estimate contingency due to systemic risks alongside
an estimation of contingency due to project-speci c risks. The project-speci c
expected value method was modi ed by basing the contingency estimation on the
expected number of realised risks according to a binomial scenario. A total cost
distribution was included in tool outputs by assuming the contingency cost equal
to the standard deviation of the cost estimate.
To aid business integration of the developed tool, study outputs included the
points in the project lifecycle model at which the tool should be applied, and the
process by which tool outputs become inputs to the enterprise risk management
system.
By following this approach, systemic and project-speci c risks are contained
in a single tool providing contingency cost and duration output on project level,
while enabling integration with reporting on program, portfolio and enterprise
level.